Unifying Low-Level and High-Level Music Similarity Measures

This paper proposes three of distance measures based on the audio content:

1. A low-level measure based on tempo-related description
2. A high-level semantic measure based on the inference of different musical dimensions by support vector machines. These dimensions include genre, culture, moods, instruments, rhythm, and tempo annotations
3. A hybrid measure which combines the above two

Evaluation:

1. Objective evaluation: By using classification benchmark as ground truth: "For each collection, we considered songs from the same class to be similar and songs from different classes to be dissimilar, and assessed the relevance of the songs’ rankings returned by each approach."
2. Subjective evaluation: The listener was presented with 5different playlists (one for each measure) generated from the same seed song. Independently for each playlist, we asked the listeners to provide1) a playlist similarity rating (six-point) and 2) a playlist inconsistency boolean answer (bipolar).